Multiinnovation Least-Squares Identification for System Modeling

A multiinnovation least-squares (MILS) identification algorithm is presented for linear regression models with unknown parameter vectors by expanding the innovation length in the traditional recursive least-squares (RLS) algorithm from the viewpoint of innovation modification. Because the proposed M...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 40; no. 3; pp. 767 - 778
Main Authors: Feng Ding, Liu, Peter X, Guangjun Liu
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2010
Subjects:
Algorithms
Analytical models
Colored noise
Computer Simulation
Convergence properties
Cybernetics
Data Interpretation, Statistical
Dynamical systems
Intervals
least squares (LS)
Least squares method
Least-Squares Analysis
Level control
Linear regression
Mathematical analysis
Modeling
Models, Statistical
multiinnovation identification
Parameter estimation
Performance analysis
recursive identification
Regression
Resonance light scattering
stochastic processes
Technological innovation
Vectors
ISSN:1083-4419, 1941-0492, 1941-0492
Online Access:Get full text
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Summary:A multiinnovation least-squares (MILS) identification algorithm is presented for linear regression models with unknown parameter vectors by expanding the innovation length in the traditional recursive least-squares (RLS) algorithm from the viewpoint of innovation modification. Because the proposed MILS algorithm uses p innovations (not only the current innovation but also past innovations) at each iteration (with the integer p > 1 being an innovation length), the accuracy of parameter estimation is improved, compared with that of the RLS algorithm. Performance analysis and simulation results show that the proposed MILS algorithm is consistently convergent. Moreover, a new interval-varying MILS algorithm is proposed, for which the key is to dynamically change the interval in order to deal with cases where some measurement data are missing. Furthermore, an auxiliary-model-based MILS algorithm is derived for pseudolinear models corresponding to output error moving average systems with colored noises. Finally, the proposed algorithms are applied to model an experimental water level control system.
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ISSN:1083-4419
1941-0492
1941-0492
DOI:10.1109/TSMCB.2009.2028871